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Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework

Abstract

This paper presents a new method for super-resolution (SR) reconstruction of a high-resolution (HR) image from several low-resolution (LR) images. The HR image is assumed to be composed of homogeneous regions. Thus, the a priori distribution of the pixels is modeled by a finite mixture model (FMM) and a Potts Markov model (PMM) for the labels. The whole a priori model is then a hierarchical Markov model. The LR images are assumed to be obtained from the HR image by lowpass filtering, arbitrarily translation, decimation, and finally corruption by a random noise. The problem is then put in a Bayesian detection and estimation framework, and appropriate algorithms are developed based on Markov chain Monte Carlo (MCMC) Gibbs sampling. At the end, we have not only an estimate of the HR image but also an estimate of the classification labels which leads to a segmentation result.

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Correspondence to Fabrice Humblot.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Humblot, F., Mohammad-Djafari, A. Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework. EURASIP J. Adv. Signal Process. 2006, 036971 (2006). https://doi.org/10.1155/ASP/2006/36971

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  • DOI: https://doi.org/10.1155/ASP/2006/36971

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